Time critical machine vision applications require high levels of speed and accuracy in the matching algorithms which determine depth. Depth estimation is typically based on stereo correspondence, the difference in coordinates of corresponding pixels in stereo images. The difference in coordinate position between a pair of corresponding pixels is referred to as the disparity, and the assimilation of differences among pairs of corresponding pixels in stereo imagery is referred to as a depth map.
The accuracy of depth mapping is dependent on accurate identification of corresponding pixels while applications, such as automatic vehicle braking, require rapid execution. Satisfactory accuracy for real time responses can require rapid execution of data intensive, iterative computations.
Conventionally, estimating depth from imagery normally begins with application of a stereo matching algorithm to construct a disparity map from a pair of images taken of the same scene from different viewpoints. Typically, the two images are acquired at the same time with two cameras residing in the same lateral plane, although a depth map may also be determined from correspondence between images of a scene captured at different times provided that spatial differences occur between corresponding pixels in the lateral plane. Generally, for depth estimations, most of the pixels of interest in one image will have a corresponding pixel in the other image.